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Last week alone, though, 1.6 million people newly applied for unemployment benefits. That’s the equivalent of 2,300 Carrier plants

Somehow, for years, the four tragic deaths in Benghazi consumed the agenda of six GOP-controlled congressional committees and the programming of the most-watched cable news channel. But today, a deadly shock magnified by government ineptitude that has led to 46,000 times as many lives lost “is what it is.”

the coronavirus death toll is about 46,000 Benghazis

According to congressional investigators, Navarro negotiated a contract that resulted in the government overpaying for ventilators by $500 million. (The contract was canceled Monday.) He also championed a $765 million federal loan to Eastman Kodak to transform it into a drugmaker. (The loan has since unraveled and is the subject of a securities investigation.)

So how many taxpayer dollars was Navarro involved in wasting through these two deals alone? Measured in units that should be familiar to consumers of right-wing news, it’s roughly two Solyndras.

The debt increase under Trump during a single term is on track to surpass that under Obama across two terms.

For each Hillary Clinton private email scandal (one), there are at least eight senior Trump officials who have reportedly used private email to conduct official business

For every Obama-era incident involving supposed retaliation against political opponents, there are literally dozens of instances of Trump trying to use the power of his office to punish perceived enemie

Quantification is the most overwhelming influence upon the contemporary American understanding of, well, everything. It is enabled by the idolatry of data, which has itself been enabled by the almost unimaginable data-generating capabilities of the new technology

The distinction between knowledge and information is a thing of the past, and there is no greater disgrace than to be a thing of the past.

even as technologism, which is not the same as technology, asserts itself over more and more precincts of human life, so too does scientism, which is not the same as science.

The notion that the nonmaterial dimensions of life must be explained in terms of the material dimensions, and that nonscientific understandings must be translated into scientific understandings if they are to qualify as knowledge, is increasingly popular inside and outside the university

The contrary insistence that the glories of art and thought are not evolutionary adaptations, or that the mind is not the brain, or that love is not just biology’s bait for sex, now amounts to a kind of heresy.

So, too, does the view that the strongest defense of the humanities lies not in the appeal to their utility — that literature majors may find good jobs, that theaters may economically revitalize neighborhoods — but rather in the appeal to their defiantly nonutilitarian character, so that individuals can know more than how things work, and develop their powers of discernment and judgment, their competence in matters of truth and goodness and beauty, to equip themselves adequately for the choices and the crucibles of private and public life.

are we becoming posthumanists?

In American culture right now, as I say, the worldview that is ascendant may be described as posthumanism.

The posthumanism of the 1970s and 1980s was more insular, an academic affair of “theory,” an insurgency of professors; our posthumanism is a way of life, a social fate.

In “The Age of the Crisis of Man: Thought and Fiction in America, 1933-1973,” the gifted essayist Mark Greif, who reveals himself to be also a skillful historian of ideas, charts the history of the 20th-century reckonings with the definition of “man.”

Dr. Rose lays the blame for our modern obsession directly at the feet of Adolphe Quetelet, a 19th-century Belgian mathematician. Quetelet was an early data cruncher, the first to apply statistical tools to large groups of people

Among his accomplishments was devising the body mass index, a ratio of weight to height that we still use to decide if people are too big or small. For him, the average was the optimal; normal was the best thing any human could ever possibly be.

Not so for one of his intellectual heirs, Francis Galton of Britain, who agreed that averages were excellent tools for understanding individuals. Ultimately, though, he came to the conclusion that the average defined not the optimal but simply the mediocre, a mark to be measured only so that it could be surpassed.

“Typing and ranking have come to seem so elementary, natural and right that we are no longer conscious of the fact that every such judgment always erases the individuality of the person being judged,”

But anyone who works with people can cite case after case in which the standard metrics disappoin

For educators, it’s all those brilliant underachiever

For doctors, it’s all the outliers who survive dire disease predictors

But if we are not averagerians, then who are we?

he sets forth a variety of alternate principles. Among them is the not-unfamiliar notion that all human characteristics are multidimensional, not only in specifics but also in time and context.

In other words, big data may have landed us in the Age of Average, but really enormous data, with many observations of a single person’s biology and behavior taken over time and in different contexts, may yield a far better understanding of that individual than do group norms.

In life and in health, different pathways may lead to the same end.

Dr. Rose spends much of his narrative in the worlds of education and business, offering up examples of schools and companies that have defied the rule of the average, to the benefit of all. His argument will resonate in many other contexts, though: Readers will be moved to examine their own averagerian prejudices, most so ingrained as to be almost invisible, all worthy of review.

By the end of World War II, however, American corporations were facing severe talent shortages. Their senior executives were growing old, and a dearth of hiring from the Depression through the war had resulted in a shortfall of able, well-trained managers. Finding people who had the potential to rise quickly through the ranks became an overriding preoccupation of American businesses. They began to devise a formal hiring-and-management system based in part on new studies of human behavior, and in part on military techniques developed during both world wars, when huge mobilization efforts and mass casualties created the need to get the right people into the right roles as efficiently as possible. By the 1950s, it was not unusual for companies to spend days with young applicants for professional jobs, conducting a battery of tests, all with an eye toward corner-office potential.

But companies abandoned their hard-edged practices for another important reason: many of their methods of evaluation turned out not to be very scientific.

this regime, so widespread in corporate America at mid-century, had almost disappeared by 1990. “I think an HR person from the late 1970s would be stunned to see how casually companies hire now,”

Many factors explain the change, he said, and then he ticked off a number of them: Increased job-switching has made it less important and less economical for companies to test so thoroughly. A heightened focus on short-term financial results has led to deep cuts in corporate functions that bear fruit only in the long term. The Civil Rights Act of 1964, which exposed companies to legal liability for discriminatory hiring practices, has made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased.

about a quarter of the country’s corporations were using similar tests to evaluate managers and junior executives, usually to assess whether they were ready for bigger roles.

He has encouraged the company’s HR executives to think about applying the games to the recruitment and evaluation of all professional workers.

Knack makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.

When the results came back, Haringa recalled, his heart began to beat a little faster. Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified the people whose ideas had panned out. The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.

What Knack is doing, Haringa told me, “is almost like a paradigm shift.” It offers a way for his GameChanger unit to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out.

Aptitude, skills, personal history, psychological stability, discretion, loyalty—companies at the time felt they had a need (and the right) to look into them all. That ambit is expanding once again, and this is undeniably unsettling. Should the ideas of scientists be dismissed because of the way they play a game? Should job candidates be ranked by what their Web habits say about them? Should the “data signature” of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.

scoring distance from work could violate equal-employment-opportunity standards. Marital status? Motherhood? Church membership? “Stuff like that,” Meyerle said, “we just don’t touch”—at least not in the U.S., where the legal environment is strict. Meyerle told me that Evolv has looked into these sorts of factors in its work for clients abroad, and that some of them produce “startling results.”

consider the alternative. A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work.

We may like to think that society has become more enlightened since those days, and in many ways it has, but our biases are mostly unconscious, and they can run surprisingly deep. Consider race. For a 2004 study called “Are Emily and Greg More Employable Than Lakisha and Jamal?,” the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago. To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.

a sociologist at Northwestern, spent parts of the three years from 2006 to 2008 interviewing professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—wait for it—shared leisure interests.

Lacking “reliable predictors of future performance,” Rivera writes, “assessors purposefully used their own experiences as models of merit.” Former college athletes “typically prized participation in varsity sports above all other types of involvement.” People who’d majored in engineering gave engineers a leg up, believing they were better prepared.

the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours. Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team

In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.

In 2010, however, Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors. (Previous experience, one of the few criteria that Xerox had explicitly screened for in the past, turns out to have no bearing on either productivity or retention

When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose. Xerox still interviews all candidates in person before deciding to hire them, Morse told me, but, she added, “We’re getting to the point where some of our hiring managers don’t even want to interview anymore”

Gone are the days, Ostberg told me, when, say, a small survey of college students would be used to predict the statistical validity of an evaluation tool. “We’ve got a data set of 347,000 actual employees who have gone through these different types of assessments or tools,” he told me, “and now we have performance-outcome data, and we can split those and slice and dice by industry and location.”

Evolv’s tests allow companies to capture data about everybody who applies for work, and everybody who gets hired—a complete data set from which sample bias, long a major vexation for industrial-organization psychologists, simply disappears. The sheer number of observations that this approach makes possible allows Evolv to say with precision which attributes matter more to the success of retail-sales workers (decisiveness, spatial orientation, persuasiveness) or customer-service personnel at call centers (rapport-building)

There are some data that Evolv simply won’t use, out of a concern that the information might lead to systematic bias against whole classes of people

the idea that hiring was a science fell out of favor. But now it’s coming back, thanks to new technologies and methods of analysis that are cheaper, faster, and much-wider-ranging than what we had before

what most excites him are the possibilities that arise from monitoring the entire life cycle of a worker at any given company.

Now the two companies are working together to marry pre-hire assessments to an increasing array of post-hire data: about not only performance and duration of service but also who trained the employees; who has managed them; whether they were promoted to a supervisory role, and how quickly; how they performed in that role; and why they eventually left.

What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.

I turned to Sandy Pentland, the director of the Human Dynamics Laboratory at MIT. In recent years, Pentland has pioneered the use of specialized electronic “badges” that transmit data about employees’ interactions as they go about their days. The badges capture all sorts of information about formal and informal conversations: their length; the tone of voice and gestures of the people involved; how much those people talk, listen, and interrupt; the degree to which they demonstrate empathy and extroversion; and more. Each badge generates about 100 data points a minute.

he tried the badges out on about 2,500 people, in 21 different organizations, and learned a number of interesting lessons. About a third of team performance, he discovered, can usually be predicted merely by the number of face-to-face exchanges among team members. (Too many is as much of a problem as too few.) Using data gathered by the badges, he was able to predict which teams would win a business-plan contest, and which workers would (rightly) say they’d had a “productive” or “creative” day. Not only that, but he claimed that his researchers had discovered the “data signature” of natural leaders, whom he called “charismatic connectors” and all of whom, he reported, circulate actively, give their time democratically to others, engage in brief but energetic conversations, and listen at least as much as they talk.

His group is developing apps to allow team members to view their own metrics more or less in real time, so that they can see, relative to the benchmarks of highly successful employees, whether they’re getting out of their offices enough, or listening enough, or spending enough time with people outside their own team.

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior.

people analytics will ultimately have a vastly larger impact on the economy than the algorithms that now trade on Wall Street or figure out which ads to show us. He reminded me that we’ve witnessed this kind of transformation before in the history of management science. Near the turn of the 20th century, both Frederick Taylor and Henry Ford famously paced the factory floor with stopwatches, to improve worker efficiency.

“The quantities of data that those earlier generations were working with,” he said, “were infinitesimal compared to what’s available now. There’s been a real sea change in the past five years, where the quantities have just grown so large—petabytes, exabytes, zetta—that you start to be able to do things you never could before.”

People analytics will unquestionably provide many workers with more options and more power. Gild, for example, helps companies find undervalued software programmers, working indirectly to raise those people’s pay. Other companies are doing similar work. One called Entelo, for instance, specializes in using algorithms to identify potentially unhappy programmers who might be receptive to a phone cal

He sees it not only as a boon to a business’s productivity and overall health but also as an important new tool that individual employees can use for self-improvement: a sort of radically expanded The 7 Habits of Highly Effective People, custom-written for each of us, or at least each type of job, in the workforce.

the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.

The way Gild arrives at these scores is not simple. The company’s algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it’s been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder’s advice is, and how widely that advice ranges.

The algorithms go further still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

having made those correlations, Gild can then score programmers who haven’t written open-source code at all, by analyzing the host of clues embedded in their online histories. They’re not all obvious, or easy to explain. Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Gild’s CEO, Sheeroy Desai, told me he believes his company’s approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects. Graphic design is one field that the company is now looking at, and many scientific, technical, and engineering roles might also fit the bill. Regardless of their occupation, most people leave “data exhaust” in their wake, a kind of digital aura that can reveal a lot about a potential hire.

professionally relevant personality traits can be judged effectively merely by scanning Facebook feeds and photos. LinkedIn, of course, captures an enormous amount of professional data and network information, across just about every profession. A controversial start-up called Klout has made its mission the measurement and public scoring of people’s online social influence.

Mullainathan expressed amazement at how little most creative and professional workers (himself included) know about what makes them effective or ineffective in the office. Most of us can’t even say with any certainty how long we’ve spent gathering information for a given project, or our pattern of information-gathering, never mind know which parts of the pattern should be reinforced, and which jettisoned. As Mullainathan put it, we don’t know our own “production function.”

Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills. In the future, Gild plans to let programmers see their own profiles and take skills challenges to try to improve their scores. It intends to show them its estimates of their market value, too, and to recommend coursework that might allow them to raise their scores even more. Not least, it plans to make accessible the scores of typical hires at specific companies, so that software engineers can better see the profile they’d need to land a particular job

Knack, for its part, is making some of its video games available to anyone with a smartphone, so people can get a better sense of their strengths, and of the fields in which their strengths would be most valued. (Palo Alto High School recently adopted the games to help students assess careers.) Ultimately, the company hopes to act as matchmaker between a large network of people who play its games (or have ever played its games) and a widening roster of corporate clients, each with its own specific profile for any given type of job.

When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers

For decades, as we’ve assessed people’s potential in the professional workforce, the most important piece of data—the one that launches careers or keeps them grounded—has been educational background: typically, whether and where people went to college, and how they did there. Over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers.

the limitations of that signal—the way it degrades with age, its overall imprecision, its many inherent biases, its extraordinary cost—are obvious. “Academic environments are artificial environments,” Laszlo Bock, Google’s senior vice president of people operations, told The New York Times in June. “People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment,” which is often quite different from the workplace.

because one’s college history is such a crucial signal in our labor market, perfectly able people who simply couldn’t sit still in a classroom at the age of 16, or who didn’t have their act together at 18, or who chose not to go to graduate school at 22, routinely get left behind for good. That such early factors so profoundly affect career arcs and hiring decisions made two or three decades later is, on its face, absurd.

I spoke with managers at a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, and nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history,

Google, too, is hiring a growing number of nongraduates. Many of the people I talked with reported that when it comes to high-paying and fast-track jobs, they’re reducing their preference for Ivy Leaguers and graduates of other highly selective schools.

This process is just beginning. Online courses are proliferating, and so are online markets that involve crowd-sourcing. Both arenas offer new opportunities for workers to build skills and showcase competence. Neither produces the kind of instantly recognizable signals of potential that a degree from a selective college, or a first job at a prestigious firm, might. That’s a problem for traditional hiring managers, because sifting through lots of small signals is so difficult and time-consuming.

all of these new developments raise philosophical questions. As professional performance becomes easier to measure and see, will we become slaves to our own status and potential, ever-focused on the metrics that tell us how and whether we are measuring up? Will too much knowledge about our limitations hinder achievement and stifle our dreams? All I can offer in response to these questions, ironically, is my own gut sense, which leads me to feel cautiously optimistic.

Google’s understanding of the promise of analytics is probably better than anybody else’s, and the company has been changing its hiring and management practices as a result of its ongoing analyses. (Brainteasers are no longer used in interviews, because they do not correlate with job success; GPA is not considered for anyone more than two years out of school, for the same reason—the list goes on.) But for all of Google’s technological enthusiasm, these same practices are still deeply human. A real, live person looks at every résumé the company receives. Hiring decisions are made by committee and are based in no small part on opinions formed during structured interviews.

As everyone in business knows, you manage what you measure. So although the replacing-GDP discussion may seem a little airy, its growing credibility in important circles could give it a real impact on economic policy

The story usually begins with Jeremy Bentham, an Englishman who in 1781 outlined a philosophy of utility that assessed the merits of an action according to how much happiness it produced. This was during the Enlightenment, when thinkers sought to replace religion-based rules with rational, scientific guides to decision making and life.

Economists, the most enthusiastic adopters of the concept, came to focus instead on the tangible expression of people’s needs and desires: what they were willing to spend money on.

For its original purpose—measuring short-term economic fluctuations—GDP is not likely to be supplanted anytime soon. It may even be gaining ground: A major discussion is under way concerning whether the U.S. Federal Reserve and other central banks should in times of crisis focus not on inflation but on GDP growth.

Compiling GDP involves making a lot of choices, and even reasonable choices can lead to skewed results. Statisticians understandably favor goods and services that are bought and sold—and thus easily valued by market price—over economic activities whose value must be estimated

Developing nations with lots of foreign direct investment saw GDP grow much faster than GNP would have—but didn’t necessarily reap the benefits, because the investments’ profits went mostly to multinational corporations.

It’s true that the challenges of tracking energy use or pollution aren’t huge. But the politics are extremely tricky. In its early days the Clinton administration pushed the Bureau of Economic Analysis—the agency that measures U.S. GDP—to develop a green GDP

Many things of value in life cannot be fully captured by GDP, but they can be measured by metrics of health, education, political freedom, and the like

The idea that economic and other data can be better presented with a dashboard of indicators than as a single number or ranked list is very much in the air among experts and policy makers.

In Sarkozy’s 2009 report on alternatives to GDP, the word “dashboard” appears 78 times. But the notion of dashboards hasn’t captured the public’s imagination. What has is a word that shows up just 29 times in the Sarkozy report (mostly in the bibliography): “happiness.”

In the 1950s and 1960s psychologists and sociologists reopened the question of whether it could be quantified. Opinion polls, then entering their heyday as measurers (and in some cases determiners) of the public mood, were an obvious vehicle for the attempt.

The economist Richard Easterlin imported the happiness discussion to his discipline with a 1974 paper pointing out that the results of national happiness polls did not correlate all that well with per capita income.

The interest in happiness surveys has also led to critical scrutiny of the Easterlin paradox. After reevaluating decades’ worth of polling data, the economists Betsey Stevenson and Justin Wolfers made headlines in 2008 by refuting the paradox—at least the part that said people in wealthy nations weren’t happier than those in poor nations

They were unable to conclusively debunk the argument that rises in income over time fail to deliver increased happiness, but the evidence they marshaled certainly muddied the waters

Money can’t buy happiness. But it could perhaps buy the ability to measure it.

A false story is much more likely to go viral than a real story, the authors find. A false story reaches 1,500 people six times quicker, on average, than a true story does.

“In short, I don’t think there’s any reason to doubt the study’s results,” said Rebekah Tromble, a professor of political science at Leiden University in the Netherlands, in an email.

It’s a question that can have life-or-death consequences.“[Fake news] has become a white-hot political and, really, cultural topic, but the trigger for us was personal events that hit Boston five years ago,” said Deb Roy, a media scientist at MIT and one of the authors of the new study.

Ultimately, they found about 126,000 tweets, which, together, had been retweeted more than 4.5 million times. Some linked to “fake” stories hosted on other websites. Some started rumors themselves, either in the text of a tweet or in an attached image. (The team used a special program that could search for words contained within static tweet images.) And some contained true information or linked to it elsewhere.

Tweet A and Tweet B both have the same size audience, but Tweet B has more “depth,” to use Vosoughi’s term. It chained together retweets, going viral in a way that Tweet A never did. “It could reach 1,000 retweets, but it has a very different shape,” he said.Here’s the thing: Fake news dominates according to both metrics. It consistently reaches a larger audience, and it tunnels much deeper into social networks than real news does. The authors found that accurate news wasn’t able to chain together more than 10 retweets. Fake news could put together a retweet chain 19 links long—and do it 10 times as fast as accurate news put together its measly 10 retweets.

What does this look like in real life? Take two examples from the last presidential election. In August 2015, a rumor circulated on social media that Donald Trump had let a sick child use his plane to get urgent medical care. Snopes confirmed almost all of the tale as true. But according to the team’s estimates, only about 1,300 people shared or retweeted the story.

Why does falsehood do so well? The MIT team settled on two hypotheses.First, fake news seems to be more “novel” than real news. Falsehoods are often notably different from the all the tweets that have appeared in a user’s timeline 60 days prior to their retweeting them, the team found.Second, fake news evokes much more emotion than the average tweet. The researchers created a database of the words that Twitter users used to reply to the 126,000 contested tweets, then analyzed it with a state-of-the-art sentiment-analysis tool. Fake tweets tended to elicit words associated with surprise and disgust, while accurate tweets summoned words associated with sadness and trust, they found.

It suggests—to me, at least, a Twitter user since 2007, and someone who got his start in journalism because of the social network—that social-media platforms do not encourage the kind of behavior that anchors a democratic government. On platforms where every user is at once a reader, a writer, and a publisher, falsehoods are too seductive not to succeed: The thrill of novelty is too alluring, the titillation of disgust too difficult to transcend. After a long and aggravating day, even the most staid user might find themselves lunging for the politically advantageous rumor. Amid an anxious election season, even the most public-minded user might subvert their higher interest to win an argument.

"United States is no longer trustworthy. It's 50% less less trustworthy than it was when we first started a survey back in 2016," said McPhillips.

The US also came in at number 15 in citizenship, quality of life and best place to visit. It ranked 17th in greenest countries, 18th in most transparent countries, and 26th in best places to travel alone -- another safety issue.

“We know that kids seek validation via the Like button. We know that it can negatively affect kids’ and teenagers’ self-esteem,” said Jim Steyer, chief executive of the nonprofit Common Sense Media. “The public is finally waking up to how many of these tactics can be manipulative.”

Instagram, the photo-sharing site owned by Facebook, also began hiding some Likes and other metrics this year as part of an experiment intended to make users feel happier about the time they spend on the platform.

This could create “a less pressurized environment where people feel comfortable expressing themselves,” Mr. Mosseri said at the time.

For the well-being of its users, Bejar argued, Meta needed to change course, focusing less on a flawed system of rules-based policing and more on addressing such bad experiences

The company would need to collect data on what upset users and then work to combat the source of it, nudging those who made others uncomfortable to improve their behavior and isolating communities of users who deliberately sought to harm others.

“I am appealing to you because I believe that working this way will require a culture shift,” Bejar wrote to Zuckerberg—the company would have to acknowledge that its existing approach to governing Facebook and Instagram wasn’t working.

During and after Bejar’s time as a consultant, Meta spokesman Andy Stone said, the company has rolled out several product features meant to address some of the Well-Being Team’s findings. Those features include warnings to users before they post comments that Meta’s automated systems flag as potentially offensive, and reminders to be kind when sending direct messages to users like content creators who receive a large volume of messages. 

Meta’s classifiers were reliable enough to remove only a low single-digit percentage of hate speech with any degree of precision.

Bejar was floored—all the more so when he learned that virtually all of his daughter’s friends had been subjected to similar harassment. “DTF?” a user they’d never met would ask, using shorthand for a vulgar proposition. Instagram acted so rarely on reports of such behavior that the girls no longer bothered reporting them. 

Meta’s own statistics suggested that big problems didn’t exist. 

Meta had come to approach governing user behavior as an overwhelmingly automated process. Engineers would compile data sets of unacceptable content—things like terrorism, pornography, bullying or “excessive gore”—and then train machine-learning models to screen future content for similar material.

While users could still flag things that upset them, Meta shifted resources away from reviewing them. To discourage users from filing reports, internal documents from 2019 show, Meta added steps to the reporting process. Meta said the changes were meant to discourage frivolous reports and educate users about platform rules. 

The outperformance of Meta’s automated enforcement relied on what Bejar considered two sleights of hand. The systems didn’t catch anywhere near the majority of banned content—only the majority of what the company ultimately removed

“Please don’t talk about my underage tits,” Bejar’s daughter shot back before reporting his comment to Instagram. A few days later, the platform got back to her: The insult didn’t violate its community guidelines.

Also buttressing Meta’s statistics were rules written narrowly enough to ban only unambiguously vile material. Meta’s rules didn’t clearly prohibit adults from flooding the comments section on a teenager’s posts with kiss emojis or posting pictures of kids in their underwear, inviting their followers to “see more” in a private Facebook Messenger group. 

“Mark personally values freedom of expression first and foremost and would say this is a feature and not a bug,” Rosen responded

Narrow rules and unreliable automated enforcement systems left a lot of room for bad behavior—but they made the company’s child-safety statistics look pretty good according to Meta’s metric of choice: prevalence.

Defined as the percentage of content viewed worldwide that explicitly violates a Meta rule, prevalence was the company’s preferred measuring stick for the problems users experienced.

According to prevalence, child exploitation was so rare on the platform that it couldn’t be reliably estimated, less than 0.05%, the threshold for functional measurement. Content deemed to encourage self-harm, such as eating disorders, was just as minimal, and rule violations for bullying and harassment occurred in just eight of 10,000 views. 

“There’s a grading-your-own-homework problem,”

Meta defines what constitutes harmful content, so it shapes the discussion of how successful it is at dealing with it.”

It could reconsider its AI-generated “beauty filters,” which internal research suggested made both the people who used them and those who viewed the images more self-critical

the team built a new questionnaire called BEEF, short for “Bad Emotional Experience Feedback.

A recurring survey of issues 238,000 users had experienced over the past seven days, the effort identified problems with prevalence from the start: Users were 100 times more likely to tell Instagram they’d witnessed bullying in the last week than Meta’s bullying-prevalence statistics indicated they should.

“People feel like they’re having a bad experience or they don’t,” one presentation on BEEF noted. “Their perception isn’t constrained by policy.

they seemed particularly common among teens on Instagram.

Among users under the age of 16, 26% recalled having a bad experience in the last week due to witnessing hostility against someone based on their race, religion or identity

More than a fifth felt worse about themselves after viewing others’ posts, and 13% had experienced unwanted sexual advances in the past seven days. 

The vast gap between the low prevalence of content deemed problematic in the company’s own statistics and what users told the company they experienced suggested that Meta’s definitions were off, Bejar argued

To minimize content that teenagers told researchers made them feel bad about themselves, Instagram could cap how much beauty- and fashion-influencer content users saw.

Proving to Meta’s leadership that the company’s prevalence metrics were missing the point was going to require data the company didn’t have. So Bejar and a group of staffers from the Well-Being Team started collecting it

And it could build ways for users to report unwanted contacts, the first step to figuring out how to discourage them.

One experiment run in response to BEEF data showed that when users were notified that their comment or post had upset people who saw it, they often deleted it of their own accord. “Even if you don’t mandate behaviors,” said Krieger, “you can at least send signals about what behaviors aren’t welcome.”

But among the ranks of Meta’s senior middle management, Bejar and Krieger said, BEEF hit a wall. Managers who had made their careers on incrementally improving prevalence statistics weren’t receptive to the suggestion that the approach wasn’t working. 

After three decades in Silicon Valley, he understood that members of the company’s C-Suite might not appreciate a damning appraisal of the safety risks young users faced from its product—especially one citing the company’s own data. 

“This was the email that my entire career in tech trained me not to send,” he says. “But a part of me was still hoping they just didn’t know.”

“Policy enforcement is analogous to the police,” he wrote in the email Oct. 5, 2021—arguing that it’s essential to respond to crime, but that it’s not what makes a community safe. Meta had an opportunity to do right by its users and take on a problem that Bejar believed was almost certainly industrywide.

fter Haugen’s airing of internal research, Meta had cracked down on the distribution of anything that would, if leaked, cause further reputational damage. With executives privately asserting that the company’s research division harbored a fifth column of detractors, Meta was formalizing a raft of new rules for employees’ internal communication.

Among the mandates for achieving “Narrative Excellence,” as the company called it, was to keep research data tight and never assert a moral or legal duty to fix a problem.

“I had to write about it as a hypothetical,” Bejar said. Rather than acknowledging that Instagram’s survey data showed that teens regularly faced unwanted sexual advances, the memo merely suggested how Instagram might help teens if they faced such a problem.

The hope that the team’s work would continue didn’t last. The company stopped conducting the specific survey behind BEEF, then laid off most everyone who’d worked on it as part of what Zuckerberg called Meta’s “year of efficiency.

If Meta was to change, Bejar told the Journal, the effort would have to come from the outside. He began consulting with a coalition of state attorneys general who filed suit against the company late last month, alleging that the company had built its products to maximize engagement at the expense of young users’ physical and mental health. Bejar also got in touch with members of Congress about where he believes the company’s user-safety efforts fell short. 

Dozens of popular books by nonexperts have filled the void, many claiming that IQ—which after more than a century remains the dominant metric for intelligence—predicts nothing important or that intelligence is simply too complex and subtle to be measured.

evidence points toward a strong genetic component in IQ. Based on studies of twins, siblings, and adoption, contemporary estimates put the heritability of IQ at 50 to 80 percent

intelligence has a genetic recipe

“Do you know any Perl?” Li asked him. Perl is a programming language often used to analyze genomic data. Zhao admitted he did not; in fact, he had no programming skills at all. Li handed him a massive textbook, Programming Perl. There were only two weeks left in the camp, so this would get rid of the kid for good. A few days later, Zhao returned. “I finished it,” he said. “The problems are kind of boring. Do you have anything harder?” Perl is a famously complicated language that takes university students a full year to learn.

So Li gave him a large DNA data set and a complicated statistical problem. That should do it. But Zhao returned later that day. “Finished.” Not only was it finished—and correct—but Zhao had even built a slick interface on top of the data.

driven by a fascination with kids who are born smart; he wants to know what makes them—and by extension, himself—the way they are.

In other words, the invisible hand did not solve the problem of politics by making politics altogether unnecessary. “We don’t think government can solve all our problems,” President Obama said in his convention address, “But we don’t think that government is the source of all our problems.” Smith would have appreciated this formulation. For him, whether government should get out of the way in any given matter, economic or otherwise, was a question for considered judgment abetted by scientific inquiry.

politics is a practical venture, and Smith distrusted those statesmen who confused their work with an exercise in speculative philosophy. Their proposals should be judged not by the delusive lights of the imagination, but by the metrics of science and experience, what President Obama described in the first presidential debate as “math, common sense and our history.”

John Paul Rollert teaches business ethics at the University of Chicago Booth School of Business and leadership at the Harvard Extension School.  He is the author of a recent paper on President Obama’s “Empathy Standard” for the Yale Law Journal Online.

Adam Smith, analytic philosophy, economics, Elections 2012

They view learning as the process of delivering learning objects for the individual consumption of students, and they have great faith that this delivery process can be optimized by algorithms and data mining. It is incredibly important for them that we have quantifiable outcomes of learning (standardized tests), since they can only optimize on quantitative metrics.

the biggest players in the Open movement generally believe that learning is a process of algorthmically delivering learning objects to consumers, and they frequently use "supply and demand" models to conceptualize their efforts

The difference between Open and Market is that Open folks believe that learning objects are not commodities to be bought and sold, but the public infrastructure of our culture

They'd like learning objects and the algorithms distributing those objects to be openly licensed and free for teachers to reuse, remix, and re-publish.

The "Dewey" people reject the notion of learning as "delivery" and the free market as the best platform for learning.

Dewey is a complex figure, but when most people invoke him, they mean that learning occurs through people's experiences and not through content delivery

Learning occurs when teachers and students work together to create or make something with meaning to to people in the real world

They tend to believe that the nuanced, contextual, social experiences that lead to the best learning experiences are easiest to facilitate when the curriculum is not overly prescriptive.

Impact Factor and the Future of Medical Journals

Impact Factor and the Future of Medical Journals

it’s impossible to know for sure, but, according to one publisher’s estimate, book sales in the U.S. now make up no more than seven per cent of the company’s roughly seventy-five billion dollars in annual revenue.

A monopoly is dangerous because it concentrates so much economic power, but in the book business the prospect of a single owner of both the means of production and the modes of distribution is especially worrisome: it would give Amazon more control over the exchange of ideas than any company in U.S. history.

“The key to understanding Amazon is the hiring process,” one former employee said. “You’re not hired to do a particular job—you’re hired to be an Amazonian. Lots of managers had to take the Myers-Briggs personality tests. Eighty per cent of them came in two or three similar categories, and Bezos is the same: introverted, detail-oriented, engineer-type personality. Not musicians, designers, salesmen. The vast majority fall within the same personality type—people who graduate at the top of their class at M.I.T. and have no idea what to say to a woman in a bar.”

According to Marcus, Amazon executives considered publishing people “antediluvian losers with rotary phones and inventory systems designed in 1968 and warehouses full of crap.” Publishers kept no data on customers, making their bets on books a matter of instinct rather than metrics. They were full of inefficiences, starting with overpriced Manhattan offices.

For a smaller house, Amazon’s total discount can go as high as sixty per cent, which cuts deeply into already slim profit margins. Because Amazon manages its inventory so well, it often buys books from small publishers with the understanding that it can’t return them, for an even deeper discount

According to one insider, around 2008—when the company was selling far more than books, and was making twenty billion dollars a year in revenue, more than the combined sales of all other American bookstores—Amazon began thinking of content as central to its business. Authors started to be considered among the company’s most important customers. By then, Amazon had lost much of the market in selling music and videos to Apple and Netflix, and its relations with publishers were deteriorating

In its drive for profitability, Amazon did not raise retail prices; it simply squeezed its suppliers harder, much as Walmart had done with manufacturers. Amazon demanded ever-larger co-op fees and better shipping terms; publishers knew that they would stop being favored by the site’s recommendation algorithms if they didn’t comply. Eventually, they all did.

Brad Stone describes one campaign to pressure the most vulnerable publishers for better terms: internally, it was known as the Gazelle Project, after Bezos suggested “that Amazon should approach these small publishers the way a cheetah would pursue a sickly gazelle.”

ithout dropping co-op fees entirely, Amazon simplified its system: publishers were asked to hand over a percentage of their previous year’s sales on the site, as “marketing development funds.”

The figure keeps rising, though less for the giant pachyderms than for the sickly gazelles. According to the marketing executive, the larger houses, which used to pay two or three per cent of their net sales through Amazon, now relinquish five to seven per cent of gross sales, pushing Amazon’s percentage discount on books into the mid-fifties. Random House currently gives Amazon an effective discount of around fifty-three per cent.

In December, 1999, at the height of the dot-com mania, Time named Bezos its Person of the Year. “Amazon isn’t about technology or even commerce,” the breathless cover article announced. “Amazon is, like every other site on the Web, a content play.” Yet this was the moment, Marcus said, when “content” people were “on the way out.”

In 2004, he set up a lab in Silicon Valley that would build Amazon’s first piece of consumer hardware: a device for reading digital books. According to Stone’s book, Bezos told the executive running the project, “Proceed as if your goal is to put everyone selling physical books out of a job.”

By 2010, Amazon controlled ninety per cent of the market in digital books—a dominance that almost no company, in any industry, could claim. Its prohibitively low prices warded off competition

Lately, digital titles have levelled off at about thirty per cent of book sales.

The literary agent Andrew Wylie (whose firm represents me) says, “What Bezos wants is to drag the retail price down as low as he can get it—a dollar-ninety-nine, even ninety-nine cents. That’s the Apple play—‘What we want is traffic through our device, and we’ll do anything to get there.’ ” If customers grew used to paying just a few dollars for an e-book, how long before publishers would have to slash the cover price of all their titles?

As Apple and the publishers see it, the ruling ignored the context of the case: when the key events occurred, Amazon effectively had a monopoly in digital books and was selling them so cheaply that it resembled predatory pricing—a barrier to entry for potential competitors. Since then, Amazon’s share of the e-book market has dropped, levelling off at about sixty-five per cent, with the rest going largely to Apple and to Barnes & Noble, which sells the Nook e-reader. In other words, before the feds stepped in, the agency model introduced competition to the market

But the court’s decision reflected a trend in legal thinking among liberals and conservatives alike, going back to the seventies, that looks at antitrust cases from the perspective of consumers, not producers: what matters is lowering prices, even if that goal comes at the expense of competition. Barry Lynn, a market-policy expert at the New America Foundation, said, “It’s one of the main factors that’s led to massive consolidation.”

The combination of ceaseless innovation and low-wage drudgery makes Amazon the epitome of a successful New Economy company. It’s hiring as fast as it can—nearly thirty thousand employees last year.

brick-and-mortar retailers employ forty-seven people for every ten million dollars in revenue earned; Amazon employs fourteen.

Since the arrival of the Kindle, the tension between Amazon and the publishers has become an open battle. The conflict reflects not only business antagonism amid technological change but a division between the two coasts, with different cultural styles and a philosophical disagreement about what techies call “disruption.”

Bezos told Charlie Rose, “Amazon is not happening to bookselling. The future is happening to bookselling.”

n Grandinetti’s view, the Kindle “has helped the book business make a more orderly transition to a mixed print and digital world than perhaps any other medium.” Compared with people who work in music, movies, and newspapers, he said, authors are well positioned to thrive. The old print world of scarcity—with a limited number of publishers and editors selecting which manuscripts to publish, and a limited number of bookstores selecting which titles to carry—is yielding to a world of digital abundance. Grandinetti told me that, in these new circumstances, a publisher’s job “is to build a megaphone.”

it offers an extremely popular self-publishing platform. Authors become Amazon partners, earning up to seventy per cent in royalties, as opposed to the fifteen per cent that authors typically make on hardcovers. Bezos touts the biggest successes, such as Theresa Ragan, whose self-published thrillers and romances have been downloaded hundreds of thousands of times. But one survey found that half of all self-published authors make less than five hundred dollars a year.

The business term for all this clear-cutting is “disintermediation”: the elimination of the “gatekeepers,” as Bezos calls the professionals who get in the customer’s way. There’s a populist inflection to Amazon’s propaganda, an argument against élitist institutions and for “the democratization of the means of production”—a common line of thought in the West Coast tech world

“Book publishing is a very human business, and Amazon is driven by algorithms and scale,” Sargent told me. When a house gets behind a new book, “well over two hundred people are pushing your book all over the place, handing it to people, talking about it. A mass of humans, all in one place, generating tremendous energy—that’s the magic potion of publishing. . . . That’s pretty hard to replicate in Amazon’s publishing world, where they have hundreds of thousands of titles.”

By producing its own original work, Amazon can sell more devices and sign up more Prime members—a major source of revenue. While the company was building the

Like the publishing venture, Amazon Studios set out to make the old “gatekeepers”—in this case, Hollywood agents and executives—obsolete. “We let the data drive what to put in front of customers,” Carr told the Wall Street Journal. “We don’t have tastemakers deciding what our customers should read, listen to, and watch.”

book publishers have been consolidating for several decades, under the ownership of media conglomerates like News Corporation, which squeeze them for profits, or holding companies such as Rivergroup, which strip them to service debt. The effect of all this corporatization, as with the replacement of independent booksellers by superstores, has been to privilege the blockbuster.

Publishers sometimes pass on this cost to authors, by redefining royalties as a percentage of the publisher’s receipts, not of the book’s list price. Recently, publishers say, Amazon began demanding an additional payment, amounting to approximately one per cent of net sales

the long-term outlook is discouraging. This is partly because Americans don’t read as many books as they used to—they are too busy doing other things with their devices—but also because of the relentless downward pressure on prices that Amazon enforces.

he digital market is awash with millions of barely edited titles, most of it dreck, while r

Amazon believes that its approach encourages ever more people to tell their stories to ever more people, and turns writers into entrepreneurs; the price per unit might be cheap, but the higher number of units sold, and the accompanying royalties, will make authors wealthier

In Friedman’s view, selling digital books at low prices will democratize reading: “What do you want as an author—to sell books to as few people as possible for as much as possible, or for as little as possible to as many readers as possible?”

The real talent, the people who are writers because they happen to be really good at writing—they aren’t going to be able to afford to do it.”

Seven-figure bidding wars still break out over potential blockbusters, even though these battles often turn out to be follies. The quest for publishing profits in an economy of scarcity drives the money toward a few big books. So does the gradual disappearance of book reviewers and knowledgeable booksellers, whose enthusiasm might have rescued a book from drowning in obscurity. When consumers are overwhelmed with choices, some experts argue, they all tend to buy the same well-known thing.

These trends point toward what the literary agent called “the rich getting richer, the poor getting poorer.” A few brand names at the top, a mass of unwashed titles down below, the middle hollowed out: the book business in the age of Amazon mirrors the widening inequality of the broader economy.

“If they did, in my opinion they would save the industry. They’d lose thirty per cent of their sales, but they would have an additional thirty per cent for every copy they sold, because they’d be selling directly to consumers. The industry thinks of itself as Procter & Gamble*. What gave publishers the idea that this was some big goddam business? It’s not—it’s a tiny little business, selling to a bunch of odd people who read.”

Bezos is right: gatekeepers are inherently élitist, and some of them have been weakened, in no small part, because of their complacency and short-term thinking. But gatekeepers are also barriers against the complete commercialization of ideas, allowing new talent the time to develop and learn to tell difficult truths. When the last gatekeeper but one is gone, will Amazon care whether a book is any good? ♦

And the kind of people who can comfortably afford to pay a nine-figure sum for a Picasso, the top 0.001 percent, say, are doing still better than that. You can draw that conclusion by reading the work of the French economists Thomas Piketty and Emmanuel Saez. Or you can form it by looking carefully at the market for the work of a certain Spanish painter.

After adjusting for inflation and using our 1 percent of net worth premise, a person would have needed $12.3 billion of wealth in 1997 dollars to afford the painting

In other words, the number of people who, by this metric, could easily afford to pay $179 million for a Picasso has increased more than fourfold since the painting was last on the market

American and European authorities may wish to put further effort into preventing art transactions from being used to launder money or evade taxes, as the economist Nouriel Roubini has argued is commonplace

l sums for a painting or sculpture should hope most of all that this basic global inequality trend — of the wealth of the ultrarich growing faster than the world population overall economy — remains intact

“Regards the rubbish archive project [an interactive exhibition examining waste in the context of climate change], xxx and I have some concerns on this exhibition particularly as it creates an opportunity for NGOs to talk about some of the issues that concern them around Shell’s operations.”

museum acknowledged its relationship with Shell, but maintained that it did not cave to any external pressure regarding the exhibit

Here we have converging evidence that these brain regions are important for trust; and if we can understand how these differences relate to specific social processes, then we may be able to develop more targeted treatment techniques for people who have deficits in social cognition

The volume of this area of the brain, which codes for emotional saliency, was greater in those that were both most trusting and least trusting of others. If something is emotionally important to us, the amygdala helps us code and remember it."

Numbers become not just part of the way we judge and assess, but the only way.

when students are researching a paper, how do they decide where to turn for the greatest expertise? Often, he said, by looking at what articles or papers online have the most hits.

“Just because we have the skills and ability to put metrics on everything doesn’t mean we should. People are ever-changing, fascinating and incredibly frustrating.”

black-and-white statistics, while arguably irrefutable in one way, really tell us almost nothing. Amazon’s rankings of book sales, for instance — which anyone can view — can vary wildly based on the sale of very few books.

“For almost anybody in the United States under the age of 25, the only models are quantifiable rankings,”

“Should it be that whatever has the most hits or the most editors makes it better than someone who spent his life studying Kant?”

The obsession with numbers, he said, means we don’t trust or even look for the intangibles that can’t be measured, like wisdom, judgment and expertise.

“What I’m most troubled by is the desire of individuals (especially myself) to constantly check up on these numbers, and to accept these measurements as a measure of something meaningful.”

“I have to stop worrying about numbers. I have to reclaim the ambiguous part of my own intelligence.”

Extensive research in many countries has demonstrated that we can build metrics around social and emotional skills that help to make them tangible for educators and policy makers

The OECD is about to publish the report "Skills for Social Progress: The Power of Social and Emotional Skills", that integrates available evidence on the importance of social and emotional skills for achieving positive life outcomes. It documents methods to measure social and emotional skills and it uses innovative analytical methods to show how social and emotional skills are drivers of social outcomes, such as health, civic engagement, and subjective well-being.

social and emotional skills are not just measurable, but also malleable. That means that schools, families and communities can play an active role in fostering these skills, they can be taught at home and school through adequate practices.

the report provides a range of examples of learning practices that worked in developing social and emotional skills. For example, teachers can enhance children's motivation, self-esteem and emotional stability by becoming effective mentors and learning facilitators.

Parents can provide warm, supportive and interactive environments through day-to-day home activities or routines

Coulter’s deeper point is that for America to truly be America, it must stand apart

The core problem with embracing soccer is that in so doing, America would become more like the rest of the world.

America’s own league, Major League Soccer, draws as many fans to its stadiums as do the NHL and NBA.

I wrote an essay entitled “The End of American Exceptionalism,” which argued that on subjects where the United States has long been seen as different, attitudes in America increasingly resemble those in Europe. Soccer is one of the best examples yet.

“Soccer,” Markovits and Hellerman argue, “was perceived by both native-born Americans and immigrants as a non-American activity at a time in American history when nativism and nationalism emerged to create a distinctly American self-image … if one liked soccer, one was viewed as at least resisting—if not outright rejecting—integration into America.”

The average age of Americans who call baseball their favorite sport is 53. Among Americans who like football best, it’s 46. Among Americans who prefer soccer, by contrast, the average age is only 37.

Old-stock Americans, in other words, were elevating baseball, football, and basketball into symbols of America’s distinct identity. Immigrants realized that embracing those sports offered a way to claim that identity for themselves. Clinging to soccer, by contrast, was a declaration that you would not melt.

why is interest in soccer rising now? Partly, because the United States is yet again witnessing mass immigration from soccer-mad nations.

the key shift is that America’s sports culture is less nativist. More native-born Americans now accept that a game invented overseas can become authentically American, and that the immigrants who love it can become authentically American too. Fewer believe that to have merit, something must be invented in the United States.

Americans today are less likely to insist that America’s way of doing things is always best. In 2002, 60 percent of Americans told the Pew Research Center that, “our culture is superior to others.” By 2011, it was down to 49 percent.

Americans over the age of 50 were 15 points more likely to say “our culture is superior” than were people over 50 in Germany, Spain, Britain, and France

Americans under 30, by contrast, were actually less likely to say “our culture is superior” than their counterparts in Germany, Spain, and Britain.

why didn’t soccer gain a foothold in the U.S. in the decades between the Civil War and World War I, when it was gaining dominance in Europe? Precisely because it was gaining dominance in Europe. The arbiters of taste in late 19th and early 20th century America wanted its national pastimes to be exceptional.

the third major pro-soccer constituency is liberals. They’re willing to embrace a European sport for the same reason they’re willing to embrace a European-style health care system: because they see no inherent value in America being an exception to the global rule

When the real-estate website Estately created a seven part index to determine a state’s love of soccer, it found that Washington State, Maryland, the District of Columbia, New York, and New Jersey—all bright blue—loved soccer best, while Alabama, Arkansas, North Dakota, Mississippi and Montana—all bright red—liked it least.

the soccer coalition—immigrants, liberals and the young—looks a lot like the Obama coalition.

Sports-wise, therefore, Democrats constitute an alliance between soccer and basketball fans while Republicans disproportionately follow baseball, golf, and NASCAR. Football, by far America’s most popular sport, crosses the aisle.)

The willingness of growing numbers of Americans to embrace soccer bespeaks their willingness to imagine a different relationship with the world. Historically, conservative foreign policy has oscillated between isolationism and imperialism. America must either retreat from the world or master it. It cannot be one among equals, bound by the same rules as everyone else

Exceptionalists view sports the same way. Coulter likes football, baseball, and basketball because America either plays them by itself, or—when other countries play against us—we dominate them.

Embracing soccer, by contrast, means embracing America’s role as merely one nation among many, without special privileges. It’s no coincidence that young Americans, in addition to liking soccer, also like the United Nations. In 2013, Pew found that Americans under 30 were 24 points more favorable to the U.N. than Americans over 50.

Millennials were also 23 points more likely than the elderly to say America should take its allies’ opinion into account even if means compromising our desires.

In embracing soccer, Americans are learning to take something we neither invented nor control, and nonetheless make it our own. It’s a skill we’re going to need in the years to come.